Browsing by Author "Muhammad Haris Ali Khan"

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    In-silico analysis of coding and non-coding variants in mettl5 gene
    (UMT, Lhr, 2023) Muhammad Haris Ali Khan
    Its relationship to intellectual impairment (ID) in people with METTL5 gene. Mutations in METTL5 have been linked to mild to severe forms of ID, which can manifest as deformities, seizures, microcephaly, short stature, and muscular hypotonia. The METTL5 gene is present on chromosome no 2. The METTL5 gene form a complex with TRMT112 and work normally. Due to knockdown of TRMT112 molecule the METTL5 gene expression is low in the body. In this study, bioinformatics approach has been used to predicted the coding and noncoding variants that effect the function, stability, splice site and non-coding region of METTL5 gene. A total of 66 missense variants in METTL5 gene were predicted to be pathogenic by CADD and Meta-SNP. These mutations were analyzed by CUPSAT, DynaMut and DUET for their effect on stability and variations were predicted pathogenic overall. 60 of the 61 variations were predicted to be affecting the function of the gene by Mutpred .4 variations p.Asp49Tyr, p.Asp103Gly, p.Leu76Trp and p.Gly130Glu were predicted by ScanProsite and NetSurfP 2.0 to affecting the PTM mechanism of the protein. These 61 missense variants were visualized by UCSF Chimera. There were no clashes found in protein structure after mutation. CADD predicted 15splice site variants, out of 15 variants 10 variants were pathogenic and further analyzed for their effect by SPiCE v2.1, Splice AI and Mutation Taster. None of variations with uncertain significance found in splice site were predicted by Splice AI, 4 out of 9 variants were predicted pathogenic by SPiCE v2.1 and 2 out of 9 variants were predicted deleterious by Mutation Taster to affecting the splicing site of the gene. The Regulome DB 2.2 was used for the analysis of non-coding variants and predicted that less effect on the TF binding site of METTL5 gene. The protein plus ligand analysis showed that 2 out of 5 mutated residues show no interaction with the ligand and 3 out of 5 mutated residues show the interaction with ligand structure. Data can be used for the early detection of mutations in METTL5 by cloning the mutation individually and determining their effect in vectors by mutagenesis. These findings contribute to our understanding of METTL5 gene representing complex disease features. This study demonstrated the importance of bioinformatics in determining highly pathogenic variants associated with the functional and structural relationship of METTL5